Aircraft Landing Scheduling Using Embedded Flower Pollination Algorithm

Mahmud, Ayman Almabruk; Satakshi,; Jeberson, Wilson

doi:10.1007/s10766-018-0602-x

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…They stated that the hybrid versions also show good results in different applications in combination with modified and other algorithms. Mahmud and Jeberson 31 showed that the FPA approach is also applicable in aviation. The researchers who applied the FPA to the aircraft landing schedule problem compared it with other approaches like KH and Cuckoo search and found that the FPA performed better.…”

Section: Methodsmentioning

confidence: 99%

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Korkmaz

Akgüngör

2021

Concurrency and Computation

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The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved. K E Y W O R D Sair transport passenger demand, artificial bee colony algorithm, butterfly optimization algorithm, crow search algorithm, flower pollination algorithm, krill herd algorithm INTRODUCTIONAir transport is one of the most preferred modes of transport by passengers in the world and in Turkey because it provides important benefits such as safety, speed, and comfort. Air transport, which has a very important place in terms of trade and tourism development, also contributes to the social development and economic growth of countries.Air transport has shown a rapid development worldwide after the Second World War. The most important reasons for this progress are the development of aircraft production technology in the war years, the desire of the pilots to transform their skills to a commercial profit, and the desire of countries to undertake development ventures by rapidly erasing the negative traces of the war. For all these reasons, passenger and freight

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Section: Methodsmentioning

confidence: 99%

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Korkmaz

Akgüngör

2021

Concurrency and Computation

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“…The problem of landing scheduling has been also considered in applications of nature-inspired algorithms. Some research suggests applying flower pollination for this problem [507,508], while others focus on GWO [509] and harmony search algorithm [510]. Abdul-Razaq and Ali have also used the bees algorithm to provide a nature-inspired landing scheduling for aircraft [511].…”

Section: Optimal Guidance and Controlmentioning

confidence: 99%

“…Publication Year Application Algorithm [481] 2022 Path and motion planning PSO [482] 2019 Path and motion planning ACO [483] 2020 Path and motion planning DE [484] 2023 Path and motion planning GWO [485] 2021 Path and motion planning GA [486] 2019 Path and motion planning BA [487] 2020 Target tracking BA [488] 2017 Optimal Control BA [489] 2013 Optimal Landing BA [490] 2017 Route evaluation CSA [491] 2019 Trajectory tracking CS [492] 2019 Trajectory planning CS [493] 2011 Path and motion planning DE, PSO, GA [494] 2005 Path and motion planning DE [495] 2016 Path and motion planning FAO, DE, PSO, GA [496] 2022 Path and motion planning FAO, DE [497] 2022 Trajectory planning FPA [498] 2012 Path and motion planning GSA [499] 2020 Path and motion planning GWO [500] 2022 Path and motion planning BOA [504] 2023 Drone-Truck path planning WWO, GA, PSO, DE, BBO, EBO [505] 2020 Optimal landing MFO, BOA, ABC [506] 2021 Optimal landing DFO, DE [507] 2020 Optimal landing FPA [508] 2018 Optimal landing FPA [509] 2021 Optimal landing GWO [510] 2017 Optimal landing HS [511] 2014 Optimal landing BA [512] 2008 Optimal landing CSA [513,514] 2019 Optimal space trajectory GA, PSO, ACO [516] 2015 Optimal space trajectory GSA [517] 2020 Optimal space control FAO [518] 2016 Optimal trajectory FAO [519] 2016 Air traffic control GSA [520] 2019 Trajectory tracking GWO…”

Section: Referencementioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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“…Given the time-varying demand and time-varying operation time of air traffic, an air traffic congestion mitigation scheme is proposed based on the performance model of mixed operation runway system. These models are quite different from the flight sequencing models considered in literature [20][21][22].…”

mentioning

confidence: 94%

Traffic flow modeling and optimization control in the approach airspace

Yun-xiang¹,

Huang²

2023

JIMO

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<p style='text-indent:20px;'>Air traffic control behaviors in civil aviation approach airspace include arrival time control, aircraft sequencing and aircraft diversion. In the airspace with heavy air traffic flow, air traffic controllers need to make the best decision for multiple flights in a given space and time range. The topic of this paper is the optimization in civil aviation approach airspace. In this paper, the flight operation involves multiple different resource, and the flight path selection is reset based on the given flight constraints to optimize the objective function. This paper presents an optimal decision model for controlling the flight flow in the approach airspace. The performance of the model is evaluated in different simulation scenarios.</p>

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Aircraft Landing Scheduling Using Embedded Flower Pollination Algorithm

Cited by 4 publications

References 20 publications

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Traffic flow modeling and optimization control in the approach airspace

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